Artificial immune systems (AISs) are computational systems that emulate the operation of the biological human immune system (HIS). AISs are in the computational problem-solving class referred to as metaheuristics. Metaheuristics categories are grouped into local search techniques, swarm intelligence, genetic algorithms and AIS. Each of these metaheuristics models is used to solve optimization problems.
The traditional AIS mimics the HIS. In the HIS, there are two main immune system subsystems. The first layer is the humoral immune system in which collectives of antibodies perform specific functions to identify and attack invading antigens. The second layer is the adaptive immune system, which identifies a new (previously unknown) antigen, develops a geometrically complementary model in order to defeat it and passes on this knowledge to the humoral immune system in the form of memory or immunity. As a known antigen attacks the host, the humoral immune system draws on the previous experience and then detects and attacks the new antigen by cloning antibodies. The HIS uses differentiated antibodies, including B cells, NK cells and T cells (memory, suppressor and killer T cells).
There are limits to the HIS. First, since it is manifest in a distributed network, it is limited to local search, with no potential for strategic planning. Its knowledge base is restricted to past experiences. Second, its response time is restricted. If an unusually aggressive antigen attacks the host, the HIS may not be prepared to ward off the intruder before the host is defeated. Third, it takes time to pass on the immunity from the adaptive immune system to the humoral immune system in the form of memory. Fourth, the HIS is easily confused. For instance, it may attack itself, a phenomenon that is manifested as an auto-immune disease. Similarly, it may overreact and manifest as an allergy. Fifth, as the host gets weaker, the immune response mechanism is suppressed, which is hardly reliable. Sixth, the HIS's high threshold for identifying and attacking an antigen may result in a reaction that is too late to be effective. Finally, it is possible to infiltrate the HIS and disable it.
The traditional AIS, drawn from the HIS to solve complex problems, abstracts the concepts of the HIS for application to computational environments. In the AIS, the artificial humoral immune system is structured as a distributed network in which information is passed to self-interested autonomous members of the collective. This layer is primarily reactive, so that antibodies are propagated on-demand in order to attack known antigens.
As the artificial adaptive immune system encounters a new antigen, it emulates the HIS in order to create a customized solution to a problem and then passes this solution to the artificial humoral immune system. The adaptive process involves learning new ways to solve problems posed by new antigens. In combination, the two layers of the traditional AIS develop a coherent system to solve optimization problems.
The AIS model provides novel approaches to solve multi-objective optimization problems. Other metaheuristic models have problem-solving limits. The local search, swarm intelligence and genetic algorithm models are limited to past experience; the AIS model, however, moves beyond the reaction-centric limits of past information constraints in problem solving. With the exception of the traditional genetic algorithm metaheuristic, all of the metaheuristic models use memory in order to learn and evolve new solutions.
Memory is used differently in each main metaheuristic model. With local search methods, memory is limited to the present analysis. With swarm intelligence methods, memory is passed between members in real time.
In the AIS, memory is passed unidirectionally from the adaptive immune system layer, which learns to solve the problem, to the humoral immune system layer, which applies the solution at the appropriate moment. Over time, the AIS maintains libraries of antigen and antibody pairings. In this way, the AIS memory, in its abstract form of immunity, is passed from the adaptive immune system layer to the humoral immune system layer. One of the challenges that the present system solves with a hybrid multilayer AIS is how to provide global information to local search optimization problems.
Learning is performed by the traditional AIS primarily in the adaptive immune system layer. An experimentation process solves the problem presented by each new antigen. Future encounters with the same antigen produce a catalytic result by triggering a cascade effect of antigens at the humoral system layer. As the antigen is further encountered, immunity is further fortified. With each new encounter of the antigen, there are fewer time lags within the linear immunity process. Moreover, as the system is optimized, it is able to solve evolving, increasingly complex problems. The information from the custom solutions generated by the adaptive immune system layer effectively restructures the humoral immune system layer by requiring less cascade reaction in the performance of the same antigen reaction function.
The traditional AIS solves problems by identifying, tracking and attacking antigens. While it can attack a known antigen, and develop a defense against a new antigen, it remains to be seen how it may anticipate a potential antigen. An AIS can solve problems that emerge from the HIS. For example, in an AIS, specific memory functions, such as the allergic overreaction or an auto-immune dysfunction, may be blocked or suppressed.
As in the HIS, there are ways to assist the traditional AIS. First, a vaccine provides a small dose of a specific antigen to allow the adaptive immune system layer to build immunity. Second, an artificial antibiotic helps the AIS to attack a specific antigen. Both models fortify the immune system defense mechanism.
Local search, swarm intelligence and genetic algorithms are useful for solving bi-objective and multi-objective optimization problems. However, because of its ability to create custom solutions to new problems and pass them on as memory for future solutions to the same problems, the general AIS may be used for evolutionary multi-objective optimization problems (eMOOPs) as well. In particular, solutions to eMOOPs are needed in complex computational combinatorial problems that involve changing and uncertain environments. In some cases, the trade-offs required in the family of solutions to eMOOPs involve temporality and shifting biases. Such cases typically consist of an interactive process in which a computational system is interacting with an indeterminate environment.
The present system provides important solutions in several categories of applications that involve eMOOPs. In particular, the present system is used for network computing, artificial neural networks, protein network modeling and evolutionary systems that involve collective behavior.
U.S. Pat. No. 5,440,723 (Arnold patent) addresses the “automatic immune system for computers and computer networks.” However, this patent, which anticipates the major developments in autonomic computing in distributed network environments, is focused only on defeating an “undesirable software entity such as a computer virus.” Similarly, U.S. Pat. No. 7,093,239 (van der Made patent) follows the Arnold patent in focusing on “detecting unwanted code in a computer system.” Consequently, the Arnold and van der Made patents seek only to emulate the performance of the HIS in identifying and attacking viruses in the network computing environment. These first automated anti-virus computer developments provide the groundwork for the present invention.
The novelties of the present invention, however, allow it to surpass the focus on network security. The present invention uses the AIS as a major category of metaheuristic to solve combinatorial problems, particularly complex eMOOPs, in a range of important network environments. The specificity of applications is detailed in this disclosure.